Deep-learning-based fault detection in building automation systems

ABSTRACT

Methods, mediums, and systems include use of a system manger application in a data processing system for fault detection a building automation system using deep learning, to receive point data for a hardware being analyzed, where the received point data is contaminated data, train a deep learning model for the hardware being analyzed, generate predicted data based on the deep learning model, analyze the predicted data and the received point data, identify a fault in the hardware being analyzed according to the received point data and the predicted data, and produce a fault report according to the identified fault.

TECHNICAL FIELD

The present disclosure is directed, in general, to automation systemsand, more particularly, to systems and methods employingmachine-learning techniques for fault detection and diagnosis (FDD) ofbuilding automation systems (BASs).

BACKGROUND OF THE DISCLOSURE

Building automation systems encompass a wide variety of systems that aidin the monitoring and control of building operations. Buildingautomation systems include security systems, fire safety systems, andcomfort systems that control environmental parameters such as heating,ventilation, and air conditioning (“HVAC”) and lighting. The elements ofa building automation system are widely dispersed throughout a facility.For example, an HVAC system may include temperature sensors andventilation damper controls, as well as other elements that are locatedin virtually every area of a facility. These building automation systemstypically have one or more centralized control stations from whichsystem data may be monitored and various aspects of system operationsmay be controlled.

To allow for monitoring and control of the dispersed control systemelements, building automation systems often employ multi-levelcommunication networks to communicate operational and/or alarminformation between operating elements, such as sensors and actuators,and the centralized control station. Several control stations connectedvia an Ethernet or another type of network may be distributed throughoutone or more building locations, each having the ability to monitor andcontrol system operations.

An important function of a management system for building automationdevices involves detecting faults and other error or abnormal conditionsin the system. Rules-based systems incorporate fundamental principles ofbuilding operation, the experience of building experts, and establishspecific expected ranges of performance for building systems. However,this approach has its drawbacks, such as hard to scale, and vaguediagnostic information. For example, FDD in a BAS typically requiresrules to be predefined and manually configured with data collected frompoints in the BAS. The more complex the rule, the more configuration ofthe rule is required. Furthermore, the more complex the rule, thegreater the chance to errors being made in the implementation of it.Improved systems and methods are desirable.

SUMMARY OF THE DISCLOSURE

Various disclosed embodiments include methods, mediums, and systems forfault detection of a building automation system using deep learning. Abuilding automation system can receive point data for a hardware beinganalyzed, where the received point data is contaminated data (that is,data that is not known to be free of faults), train a deep learningmodel for the hardware being analyzed, generate predicted data based onthe deep learning model, analyze the predicted data and the receivedpoint data, identify a fault in the hardware being analyzed according tothe received point data and the predicted data, and produce a faultreport according to the identified fault.

Another embodiment includes a non-transitory computer-readable mediumencoded with executable instructions that is configured to run in a dataprocessing system of a management system, configured to performfunctions and perform processes as described herein. Another embodimentincludes a building automation system including a data processingsystem, and a plurality of devices, sensors, and actuators, where thedata processing system executes a system manager application to performfunctions and to perform processes as described herein.

In various embodiments, the received data includes at least requiredpoints for the hardware being analyzed. In various embodiments, trainingthe deep learning model includes applying a Huber loss function. Invarious embodiments, training the deep learning model includes applyingdropout techniques for regularization to the received point data. Invarious embodiments, analyzing the predicted data and the received pointdata includes include applying cumulative sum control chart (CUSUM)sequential analysis for summing, weighting, and change detection. Invarious embodiments, the system manager application also normalizes someor all of the data, such as normalizing the deviation between thepredicted data and the received point data or normalizing some or all ofthe predicted data or the received point data. In various embodiments,identifying the fault includes comparing the received point data for afirst period of time with the predicted data for a corresponding secondperiod of time. In various embodiments, identifying the fault includesidentifying when the normalized deviation between received point dataand the predicted data is greater than more than a predeterminedthreshold. In various embodiments, the fault report is a graphic userinterface illustrating the received point data as compared to thepredicted data.

The foregoing has outlined rather broadly the features and technicaladvantages of the present disclosure so that those skilled in the artmay better understand the detailed description that follows. Additionalfeatures and advantages of the disclosure will be described hereinafterthat form the subject of the claims. Those of ordinary skill in the artwill appreciate that they may readily use the conceptions and thespecific embodiments disclosed as a basis for modifying or designingother structures for carrying out the same purposes of the presentdisclosure. Those skilled in the art will also realize that suchequivalent constructions do not depart from the spirit and scope of thedisclosure in its broadest form.

Before undertaking the DETAILED DESCRIPTION below, it may beadvantageous to set forth definitions of certain words or phrases usedthroughout this patent document: the terms “include” and “comprise,” aswell as derivatives thereof, mean inclusion without limitation; the term“or” is inclusive, meaning and/or; and the phrases “associated with” and“associated therewith,” as well as derivatives thereof, may mean toinclude, be included within, interconnect with, contain, be containedwithin, connect to or with, couple to or with, be communicable with,cooperate with, interleave, juxtapose, be proximate to, be bound to orwith, have, have a property of, or the like; and the term “controller”means any device, system or part thereof that controls at least oneoperation, whether such a device is implemented in hardware, firmware,software or some combination of at least two of the same. It should benoted that the functionality associated with any particular controllermay be centralized or distributed, whether locally or remotely.Definitions for certain words and phrases are provided throughout thispatent document, and those of ordinary skill in the art will understandthat such definitions apply in many, if not most, instances to prior aswell as future uses of such defined words and phrases. While some termsmay include a wide variety of embodiments, the appended claims mayexpressly limit these terms to specific embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, wherein likenumbers designate like objects, and in which:

FIG. 1 illustrates a block diagram of a management system in accordancewith disclosed embodiments;

FIG. 2 illustrates a block diagram of a data processing system that maybe employed in the management system in FIG. 1 in accordance withdisclosed embodiments;

FIG. 3 illustrates an example of a comfort device in accordance withdisclosed embodiments;

FIG. 4 illustrates depicts a flowchart of a process performed in themanagement system in accordance with disclosed embodiments; and

FIGS. 5 and 6 illustrate examples of graphical user interfaces inaccordance with disclose embodiments.

DETAILED DESCRIPTION

FIGS. 1 through 6, discussed below, and the various embodiments used todescribe the principles of the present disclosure in this patentdocument are by way of illustration only and should not be construed inany way to limit the scope of the disclosure. Those skilled in the artwill understand that the principles of the present disclosure may beimplemented in any suitably arranged device or system.

Rules-based fault detection is limited in its ability to scale to largesystems, where each rule must be defined for each potential fault, andin providing specific feedback as to faults.

Machine learning is a subfield of artificial intelligence, leveragingself-learning algorithms to gain knowledge from data, which eventuallyhelps decision makers arrive at more informed decisions. Machinelearning offers a more efficient way of capturing knowledge from dataand making predictions, as it relieves humans from deriving rules andbuilding models manually from empirical analysis of data. Applyingmachine learning to the field of building science, disclosed embodimentscan mine inherent relationships between several trended points, makepredictions, investigate any deviation of actual measurements frompredictions, and identify faulty data. Deep learning is a type ofmachine learning that applies artificial intelligence techniques inprocessing data and creating patterns for use in decision making. Deeplearning can use networks capable of learning unsupervised from datathat is unstructured or unlabeled.

Some machine learning approaches involve a two-step process. First, thesystem must be trained using a statistical model from normal operationwith fault-free data. Then, the system can make predictions with thismodel against operational data during the analysis period to look for apotential fault. Such an approach has several drawbacks. For example,fault free data is difficult or impossible to obtain in real worldprojects. Further, the two-step process is cumbersome. Moreover, if thesystem is faced with operational conditions not covered by the faultfree training data, false alarm problems in the fault detection phasewill be very likely.

Disclosed embodiments include systems and methods in which all dataduring the analysis period, which in any real-world case is potentiallyfaulty, is trained with a deep machine learning model. A daily metriccan be computed and analyzed to detect a fault or other anomaly, sinceon a “faulty” day, this metric will be considerably larger than on anormal day. Such an approach combines the two steps in one, andaddresses all drawbacks mentioned above. In specific implementations, agraphics processing unit (GPU) can be used to perform particularfunctions to improve performance.

FIG. 1 illustrates a block diagram of management system 100 in whichvarious embodiments of the present disclosure are implemented. In thisillustrative embodiment, the management system 100 includes a dataprocessing system 102 connected, via a management level network (MLN)104 to various other data processing systems and other devices in themanagement system 100. MLN 104 may include any number of suitableconnections, such as wired, wireless, or fiber optic links. MLN 104 maybe implemented as a number of different types of networks, such as, forexample, the Internet, a local area network (LAN), or a wide areanetwork (WAN). In some embodiments, elements of the management system100 may be implemented in a cloud computing environment. For example,MLN 104 may include or be connected to one or more routers, gateways,switches, and/or data processing systems that are remotely located in acloud computing environment.

In this illustrative embodiment, data processing system 102 is operablyconnected to comfort system 108, security system 110, and safety system112 via building level network (BLN) 114. The comfort system 108 is anenvironmental control system that controls at least one of a pluralityof environmental parameters within a building or buildings, such as, forexample, temperature, humidity, and/or lighting. The security system 110controls elements of security within a building or buildings, such as,for example, location access, monitoring, and intrusion detection. Thesafety system 112 controls elements of safety within a building orbuildings, such as, for example, smoke, fire, and/or toxic gasdetection.

As depicted, the comfort system 108 includes comfort devices 116, thesecurity system 110 includes security devices 118, and the safety system112 includes safety devices 120. The devices 116-120 may be locatedinside or in proximity to one or more buildings under the control of themanagement system 100. The devices 116-120 are configured to provide,monitor, and/or control functions of the comfort system 108, thesecurity system 110, and/or the safety system 112 within one or morebuildings managed using the management system 100. For example, withoutlimitation, the devices 116-120 may include one or more field panels,field controllers, and/or field devices inside or in proximity to one ormore buildings. More specifically, devices 116-120 may include one ormore general-purpose data processing systems, programmable controllers,routers, switches, sensors, actuators, cameras, lights, digitalthermostats, temperature sensors, fans, damper actuators, heaters,chillers, HVAC devices, detectors, motion sensors, glass-break sensors,security alarms, door/window sensors, smoke alarms, fire alarms, gasdetectors, etc. The devices 116-120 may use the BLN 114 to exchangeinformation with other components connected to the BLN 114, such as, forexample, components within the comfort system 108, the security system110, the safety system 112, and/or the data processing system 102. Fielddevices (such as sensors, actuators, cameras, light devices, heaters,chillers and other HVAC, security and fire safety devices may beconnected via a field level network to a field panel or field controllerfor monitoring and controlling the respective field devices within aroom, floor or other space of a building.

Various embodiments of the present disclosure are implemented in themanagement system 100. The management system 100 allows for systems anddevices located throughout one or more buildings to be managed,monitored, and controlled from a single point and in a uniform manner.For example, a system manager application 122 may be installed on a dataprocessing system 102. In some embodiments, system manager application122 may be an application framework as described in PCT ApplicationSerial No. PCT/US2011/054141, entitled “Management System with VersatileDisplay” and U.S. Provisional Patent Application Ser. No. 61/541,925,entitled “Management System Using Function Abstraction for OutputGeneration,” both hereby incorporated by reference. The system managerapplication 122 is a collection of software and associated data files.The system manager application 122 may include, for example, withoutlimitation, executable files, user layout definition files, rules files,graphics control modules, an infrastructure interface, and/or a numberof software extensions. The system manager application 122 provides auser-modifiable and intuitive graphical user interface for allowing auser to monitor, review, and control various building automation systemdevices. The system manager application 122 provides a user-modifiableand intuitive graphical user interface for interacting with a user usingtrend views as described herein.

The data processing system 102 includes a database 124 that storesinformation about the devices 116-120 within the management system 100.A database 124 includes one or more data models of data points, devices,and other objects monitored and controlled by the management system 100.For example, the database 124 may store values for devices in thecomfort system 108 (e.g., temperature, alarm status, humidity). Thesevalues may each be referred to as a point or data point. The database124 may also store static information, such as, model numbers, devicetypes, and/or building and room installation location information aboutdevices in the management system 100. The database 124 may also storegraphical models of one or more buildings managed by the managementsystem 100. For example, the graphical models may include layouts andschematics of one or more rooms, floors, and buildings managed by themanagement system 100.

In these illustrative embodiments, objects associated with themanagement system 100 include anything that creates, processes, orstores information regarding data points, such as physical devices(controllers, field panels, sensors, actuators, cameras, etc.) andmaintains data files, such as control schedules, trend reports, definedsystem hierarchies, and the like.

The system manager application 122 may further include softwareextensions or services that provide operations of the management system100. For example, the software extensions may include a print manager, areporting subsystem, and a status propagation manager. For example, areporting subsystem implemented on a workstation data processing system102 is a system that manages the acquisition of data values from thedatabase 124 used in the generation of reports as well as comparativetrend views.

The data processing system 102 is connected to the BLN 114 and includesone or more hardware and/or software interfaces for sending andreceiving information to and from the devices 116-120 in the comfortsystem 108, the security system 110, and/or the safety system 112. Forexample, the data processing system 102 may request and receive dataregarding a status of one or more devices in the devices 116-120. Thesystem manager application 122, via data processing system 102, alsoprovides a user with the functionality to monitor real-time informationabout the status of one or more devices and objects associated with themanagement system 100. The client manager application 122, via serverdata processing system 102 or client data processing system 106, alsoprovides a user with the functionality to issue commands to control oneor more devices and objects associated with the management system 100.For example, one or more of the devices 116-120 may operate on a networkprotocol for exchanging information with the management system, such asBACnet or LonTalk.

The illustration of the management system 100 in FIG. 1 is not meant toimply physical or architectural limitations to the manner in whichdifferent illustrative embodiments may be implemented. Other componentsin addition to and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some illustrative embodiments. Forexample, any number of data processing systems may be used asworkstations in the management system 100, while functions of the systemmanager application 122 may be implemented in different data processingsystems in the management system 100. In other examples, the buildingautomation systems controlled by the management system 100 may notinclude one or more of the comfort system 108, the security system 110,and/or the safety system 112.

FIG. 2 depicts a block diagram of a data processing system 200 in whichvarious embodiments are implemented. The data processing system 200 isan example of one implementation of the server data processing system102 in FIG. 1.

The data processing system 200 includes a processor 202 connected to alevel two cache/bridge 204, which is connected in turn to a local systembus 206. The local system bus 206 may be, for example, a peripheralcomponent interconnect (PCI) architecture bus. Also connected to thelocal system bus 206 in the depicted example are a main memory 208 and agraphics adapter 210. The graphics adapter 210 may be connected to adisplay 211.

Other peripherals, such as a local area network (LAN)/Wide Area Network(WAN)/Wireless (e.g. WiFi) adapter 212, may also be connected to thelocal system bus 206. An expansion bus interface 214 connects the localsystem bus 206 to an input/output (I/O) bus 216. The I/O bus 216 isconnected to a keyboard/mouse adapter 218, a disk controller 220, and anI/O adapter 222. The disk controller 220 may be connected to a storage226, which may be any suitable machine-usable or machine-readablestorage medium, including, but not limited to, nonvolatile, hard-codedtype mediums, such as read only memories (ROMs) or erasable,electrically programmable read only memories (EEPROMs), magnetic tapestorage, and user-recordable type mediums, such as floppy disks, harddisk drives, and compact disk read only memories (CD-ROMs) or digitalversatile disks (DVDs), and other known optical, electrical, or magneticstorage devices.

Also connected to the I/O bus 216 in the example shown is an audioadapter 224, to which speakers (not shown) may be connected for playingsounds. The keyboard/mouse adapter 218 provides a connection for apointing device (not shown), such as a mouse, trackball, trackpointer,etc. In some embodiments, the data processing system 200 may beimplemented as a touch screen device, such as, for example, a tabletcomputer or a touch screen panel. In these embodiments, elements of thekeyboard/mouse adapter 218 may be implemented in connection with thedisplay 211.

In various embodiments of the present disclosure, the data processingsystem 200 is implemented as a workstation with all or portions of asystem manager application 122 installed in the memory 208 as a systemmanager application 228. The system manager application 228 is anexample of one embodiment of system manager application 122 in FIG. 1.For example, the processor 202 executes program code of the systemmanager application 228 to generate graphical interface 230 displayed ondisplay 211. In various embodiments of the present disclosure, thegraphical user interface 230 provides an interface for a user to viewinformation about and control one or more devices, objects, and/orpoints associated with the management system 100. The graphical userinterface 230 also provides an interface that is customizable to presentthe information and the controls in an intuitive and user-modifiablemanner.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIG. 2 may vary for particular implementations. For example,other peripheral devices, such as an optical disk drive and the like,also may be used in addition to or in place of the hardware depicted.The depicted example is provided for the purpose of explanation only andis not meant to imply architectural limitations with respect to thepresent disclosure.

One of various commercial operating systems, such as a version ofMicrosoft Windows™, a product of Microsoft Corporation located inRedmond, Wash., may be employed if suitably modified. The operatingsystem may be modified or created in accordance with the presentdisclosure as described, for example, to implement discovery of objectsand generation of hierarchies for the discovered objects.

The LAN/WAN/Wifi adapter 212 may be connected to a network 232, such as,for example, MLN 104 in FIG. 1. As further explained below, the network232 may be any public or private data processing system network orcombination of networks known to those of skill in the art, includingthe Internet. Data processing system 200 may communicate over network232 to one or more computers, which are also not part of the dataprocessing system 200, but may be implemented, for example, as aseparate data processing system 200.

In various embodiments, system manager application 122 may, via dataprocessing system 102, generate reports of both current trends of valuesas well as historical trends of values generated within the devicesmonitored by the management system 100 and display graphicalrepresentations of such trends of values on a graphical user interface230. Further, system manager application 122 may, via data processingsystem 102, display an analysis of trend data and other data, includingan identification of any anomalies, faults, or other issues as describedherein. In addition, in various embodiments, system manager application122 may, via data processing system 102, automatically generate graphs,tables, charts, or graphic simulations of historical system data inaccordance with the embodiments disclosed herein. Simulations caninclude a graphical representation of appropriate system devices withlabels, colors, or other indicators to represent the data beingreplayed.

FIG. 3 illustrates an example of a comfort device 116 in accordance withdisclosed embodiments, in this example an air handling unit 302. Eachcomfort device 116, or other device described above in the managementsystem, can have one or more sensors 304, actuators 306, or controllers308. Each controller 308 can have one or more associated functions 310that control, monitor, or otherwise interact with the sensors 304 andactuators 306. Sensors 304 can include any sensors used in thecorresponding device, such as thermometers, pressure sensors, airflowsensors, safety sensors such as fire or smoke detectors, motion sensors,heat sensors, or otherwise. Actuators 306 can include any controllabledevice, such as solenoids, switches, motors, etc. The controller 308 cancommunicate with data processing system 102, and in some embodiments,data processing system 102 directly acts as the control 308. Thisparticular, non-limiting example of an air handling unit 302 illustrateselements such as the return air, outdoor air, mixing section, filter,preheat coil, bag filter, cooling coil, reheat coil, fan, and supplyair.

In particular, data can be stored for each of the sensors 304, actuators306, controllers 308, or functions 310 that indicate the state,operation, or readings of each of these components, and this data can bestored in database 124 or another storage. This data can include aplurality of data points for each of these elements. This data is usedby functions 310, controller 308, and data processing system 102 tooperate and monitor the management system, including performing FDDprocesses as disclosed herein. Of course, these particular sensors,actuators, controllers, and functions are for purposes of illustration,but each of the particular process implementations can use data from itsown sensors, actuators, controllers, or functions, as described below.

A noted above, systems and methods as disclosed herein leveragesstate-of-the-art “deep learning” libraries. Deep learning is aparticular kind of machine learning that achieves great power andflexibility by learning to represent the world as a nested hierarchy ofconcepts. In other words, it allows computers to learn complicatedconcepts by building them out of simpler ones.

Various embodiments can be implemented using GPU computing forperformance. For example, to analyze years of building automation data,computational time could become a performance bottleneck. A computersystem graphics card as can be viewed as a small computer cluster insidethe machine that uses the GPU(s) for computationally-intensive tasks. AGPU is relatively cheap compared to state-of-the-art CPUs. For example,the current market at 70% of the price of a modern CPU, a GPU can beobtained that has 450× more cores and is capable of 15× morefloating-point calculations per second. In processes as disclosedherein, GPU computing can be 10× faster than CPU computing.

Further, contrary to other approaches, the intelligent processesdisclosed herein avoid the requirement of trend data free of faults byusing robust estimation techniques. Traditional machine learning basedFDD requires trend data to be free of faults; otherwise the faultdetection power will be greatly undermined, and so requires a two-stepprocess of training and detection. The disclosed estimation techniquessimplify the workflow while not strictly requiring received point datato be free of faults.

Processes as described herein are more scalable and adaptable over timewith changes in building operation, as compared to other approaches.They are more suitable for catching early faults or degradation faultsand provide more actionable fault diagnostic information down tosub-system level. For instance, in a building automation implementation,a system as described herein can clearly indicate faults associated withsub-systems inside an air handling unit (AHU), such as the mixing box orthe cooling coil system.

The description below includes several implementation examples for usein building automation systems, including a mixing box FDD, a fan FDD, aheating coil FDD, a cooling coil FDD, and a site energy FDD, but thetechniques disclosed herein are not limited to these examples. The firstfour examples detect and diagnose whether a specific air handling unit(AHU) is subject to faults associated with these subsystems, while thesite energy example discovers abnormal energy consumption at the sitelevel.

For a specific implementation, the specific points may differ. Asdescribed below, the system can receive, from a user, a selection of anFDD analysis to perform, and in response, display to the user the pointused for that example. Examples of point function requirement levels areexplained in the following table, but these examples are non-limiting tothe overall processes described herein:

TABLE 1 Requirement Level Explanation Preferred Point Process prefersthis point, or instead would require some other points in its place.Alternative Point If preferred point is not available, these points cantake its place. Required Point Process requires this point to runOptional Point Process can run without this point, but would be moreaccurate with this point.

Each of the exemplary implementations can use its own set or requiredpoints. For example, a mixing box FDD implementation can have thefollowing required points:

-   -   Supply Airflow    -   Outdoor Air Damper Command    -   Outdoor Airflow    -   Outdoor Air Temperature    -   Mix Air Temperature    -   Return Air Temperature

In a mixing box FDD implementation, the system detects faults such asstuck/leaky damper, by studying the relationship between outdoor airflowand supply airflow, given a certain OA damper command.

As another example, a fan FDD implementation can have the followingrequired points:

-   -   Supply Airflow    -   Supply Fan Power    -   Supply Fan VFD Speed Command

In a fan FDD implementation, the system detects fan belt slipping faultsby studying the relationship between fan power and supply airflow, givena certain fan speed command.

As another example, a heating coil FDD implementation can have thefollowing required points:

-   -   Supply Airflow    -   Supply Air Temperature    -   Supply Air Temperature Setpoint    -   Mix Air Temperature    -   Cooling Coil Valve Command    -   Heating Coil Valve Command    -   Boiler Hot Water Supply Temperature    -   Boiler Hot Water Supply Temperature Setpoint    -   Hot Water Loop Differential Pressure    -   Hot Water Loop Differential Pressure Setpoint

In a heating coil FDD implementation, the system detects faults such asdeteriorated coil and stuck/leaky valve by studying the coil inlet andoutlet condition, given a certain supply airflow.

As another example, a cooling coil FDD implementation can have thefollowing required points:

-   -   Supply Airflow    -   Supply Air Temperature    -   Supply Air Temperature Setpoint    -   Cooling Coil Valve Command    -   Heating Coil Valve Command    -   Outdoor Air Temperature    -   Mix Air Temperature    -   Return Air Temperature    -   Outdoor Air Relative Humidity    -   Return Air Relative Humidity    -   Chiller Leaving CHW Temperature    -   Chiller Leaving CHW Temperature Setpoint    -   CHW Loop Differential Pressure    -   CHW Loop Differential Pressure Setpoint

In a cooling coil FDD implementation, the system detects faults such asdeteriorated coil and stuck/leaky valve, by studying the coil inlet andoutlet condition, given a certain supply airflow.

As another example, a site energy FDD implementation can have thefollowing required points:

-   -   Outdoor Air Temperature    -   Site Total Power

In a site energy FDD implementation, the system detects non-standardsite energy consumption.

To build an initial model, the system can load a set of data to beprocessed, such as data from each of the required points and/or thepreferred points, alternate points, or optional points. In one exemplaryimplementation, this can include loading a “trend interval report” foran APOGEE® Insight building automation system from Siemens BuildingTechnologies, Inc. (Buffalo Grove, Ill.) that includes data according tothe point for that particular process as described above. The system canfurther interact with a user to map available point from the data fileto those required by the disclosed processes.

After or while performing processes as described herein, the system candisplay the progress of the current machine learning process and anyprevious analysis results. A diagnostics display can include messagessuch as a warning of too many missing data and data quality issue withsensors. A fault summary display can include core findings of themachine learning-based FDD process. For each detected fault, it caninclude the dates and times on which the fault occurred. It can displaya comparison between predicted values and actual measurements, so theuser can see that on identified faulty days, deviation between actualand predicted values is larger than on normal days. It can also displayall related point functions associated with this process, enabling theuser to zoom in and examine in greater detail. The system can alsoexport all results into a document or to another system for review orfurther analysis.

FIG. 4 depicts a flowchart of an exemplary set of operations that may beexecuted by a management system to perform fault detection using deeplearning techniques as described herein. The process may be implementedby executable instructions stored in a non-transitory computer-readablemedium that cause one or more data processing systems to perform such aprocess. For example, the system manager application 122 that ismaintained in a data processing system of a management system maycomprise the executable instructions to cause one or more dataprocessing systems to perform such a process. For ease of reference,these are generically referred to as the “system” below, and the systemcan, for example, run the system manager application to perform theprocesses described below.

The system receives point data for a hardware being analyzed (402). Thepoint data can be historical data and in particular can be“contaminated” data. “Contaminated” data, as used herein, refers to datathat has not been cleaned and may include fault data (that is, data thatalready reflects a fault condition) or errors, which provides animmediate advantage over systems that require only “clean” data that hasbeen verified to be fault- and error-free. The point data includes atleast required points as described herein, and can include alternativepoints, preferred points, or optional points as described herein, or canbe other points as may be appropriate for the particular implementation.When the received point data does not expressly identify the points asdescribed herein, the system can also receive mapping information formapping the received point data to the required points and/or otherpoints. The hardware being analyzed can be any device or system beinganalyzed, including in particular building automation elements,components, subsystems, or systems, such as the mixing box, fan, heatingcoil, and cooling coil subsystem examples described above, or the siteenergy analysis for the overall site building automation systemdescribed above. The point data can reflect the data for the hardwarebeing analyzed over a predetermined first period of time, for exampleduring a typical business day, a week, or a month.

The system trains a deep learning model for the hardware being analyzed(404). In particular, for training the model, the system can apply aHuber loss function to the deep learning model and can apply dropouttechniques for regularization to the deep learning model. Thesetechniques are known to those of skill in the art, although not in thecontext of the processes described herein. The Huber loss functiondescribes the penalty incurred by specific estimation procedures and isused in robust regression since it is less sensitive to outliers indata. The Huber loss function can be used in a process as describedherein for guiding the model training process based on stochasticgradient descent. A dropout technique is useful for addressing theproblem of overfitting in deep neural networks, and is described, forexample, in Dropout: A Simple Way to Prevent Neural Networks fromOverfitting by Srivastava, et al., Journal of Machine Learning Research15 (2014) 1929-1958, hereby incorporated by reference. Using thesetechniques help enable the system to generate an accurate deep learningmodel even with contaminated received data, by compensating for flaws orerrors reflected in the data.

The system generates predicted data based on the deep learning model(406). After building the model as described above, the system generatespredicted data for one or more of the points described herein. Thepredicted data can be generated for a predetermined second period oftime, for example for a typical business day, a week, or a month.

The system normalizes and analyzes the predicted data and/or thereceived data (408). This process can include normalizing some or all ofthe data to ensure that any comparisons are valid, such as normalizingthe deviation between the predicted data and the received point data ornormalizing some or all of the predicted data or the received pointdata. This process can also include applying the cumulative sum controlchart (CUSUM) sequential analysis to the data for summing, weighting,and change detection. CUSUM techniques are known to those of skill inthe art, although not in the context of the processes described herein.An effect of applying CUSUM techniques is that any faults or abnormalpoint data is more evident.

The system identifies faults in the hardware being analyzed according tothe received point data and the predicted data (410). This can includecomparing the received point data for the first period of time with thepredicted data for the corresponding second period of time. This caninclude where the first period of time, for the received point data, isthe same as the second period of time, for the predicted data, such asspan of specific hours, a given business (occupied) day, weekend (orunoccupied) day, a week, a month, or otherwise.

Identifying the faults can include identifying differences between thereceived point data and the second point data (for example, forcorresponding points and/or times) to determine if the difference isgreater than a threshold difference or can use the normalized deviationto detect fault. When the difference is greater than a predeterminedthreshold difference, then the system can identify a fault. Similarly,when the normalized deviation between received point data and thepredicted data is greater than more than a predetermined threshold, thesystem can identify a fault. As an example, the actual, receivedtemperature point may be significantly higher or lower than thepredicted temperature point, and a fault is determined if the differenceexceeds a threshold. As another example, the actual, received airflowpoint in a mix box may be significantly higher or lower than thepredicted airflow point, and a fault is determined if the differenceexceeds a threshold.

In other cases, the system can apply more sophisticated rules todetermine a fault, similar to rules-based approaches and not required indeep-learning implementations. For example, to determine a faultcorresponding to a fouled cooling coil, the system could determine IFcooling valve is at 100%, AND supply air temperature is above set pointand the predicted supply air temperature by more than a threshold, THENidentify a fault. As another example to determine a fault correspondingto a fouled cooling coil, the system can determine the cooling systemnormal behavior (predicted data) as ΔT in relation to valve position,chilled water temperature, and supply air flow, then the system canidentify anomalies, such as the valve opening more to achieve same ΔT(or, correspondingly, that the ΔT is missed by more than a threshold atthe same valve position, chilled water temperature, and supply airflow).

The system produces a fault report (or reports) according to theidentified fault(s) (512). This can include generating a GUI asdescribed herein that identifies the faults, and can include creatingand storing, printing, or transmitting such a report. The fault reportcan represent both the effect of fault severity and the duration of thefault.

Note in particular that the process described above does not requireseparate processes to first develop the model using “clean” data, andthen a separate process to analyze real, contaminated data to detect anyfaults. Instead, this disclosed process can develop a model directlyfrom the contaminated, actual data, then detect the faults within thatvery data, in one integrated process. This provides a significant andspecific improvement in the building automation system by providing muchmore robust and efficient fault-detection capabilities.

FIG. 5 illustrates an example of a graphical user interface 500 inaccordance with disclose embodiments, for selecting the process to beperformed as disclosed herein. In this example, GUI 500 includes aprocess selection area 502, where a user can select the FDD process tobe performed. GUI 500 includes a process description area 504, where thesystem displays a description of the selected FDD process. GUI 500includes a list of required points 506, where the system displays therequired, preferred, alternate, or optional points for the selected FDDprocess.

Of course, those of skill in the art will recognize that the GUIsdescribed or illustrated herein are for example purposes only and arenon-limiting.

FIG. 6 illustrates an example of a graphical user interface 600 inaccordance with disclose embodiments, as a fault report as disclosedherein. In this example, for a mixing box FDD, a fault summary is shownwith the actual received/measured point data in the lower line 602 ascompared to the deep learning model predicted data in the upper line604, where this particular chart example represents the outdoor airflowover a span of hours on a given date. The specific timeframe representsthe first period of time, for the received/measured point data, and thecorresponding second period of time, for the deep learning modelpredicted data.

Disclosed embodiments improve the functionality and operation of themanagement system as disclosed herein. Improvements over rule-basedsystem include more scalable and adaptable analysis over time withchanges in building operation, a greater ability to catch early faultsor degradation faults, and more actionable fault diagnostic informationdown to sub-system level. Some technical features that contribute tothis are the application of state-of-the-art “deep learning” librariesand GPU computing for performance.

Those skilled in the art will recognize that, for simplicity andclarity, the full structure and operation of all data processing systemssuitable for use with the present disclosure are not being depicted ordescribed herein. Instead, only so much of a management system as isunique to the present disclosure or necessary for an understanding ofthe present disclosure is depicted and described. The remainder of theconstruction and operation of management system 100 may conform to anyof the various current implementations and practices known in the art.

Moreover, none of the various features or processes described hereinshould be considered essential to any or all embodiments, except asdescribed below. Various features may be omitted or duplicated invarious embodiments. Various processes described above may be omitted,repeated, performed sequentially, concurrently, or in a different order.Various features and processes described herein can be combined in stillother embodiments as may be described in the claims.

It is important to note that while the disclosure includes a descriptionin the context of a fully functional system, those skilled in the artwill appreciate that at least portions of the mechanism of the presentdisclosure are capable of being distributed in the form of instructionscontained within a machine-usable, computer-usable, or computer-readablemedium in any of a variety of forms, and that the present disclosureapplies equally regardless of the particular type of instruction orsignal bearing medium or storage medium utilized to actually carry outthe distribution. Examples of machine usable/readable or computerusable/readable mediums include: nonvolatile, hard-coded type mediumssuch as read only memories (ROMs) or erasable, electrically programmableread only memories (EEPROMs), and user-recordable type mediums such asfloppy disks, hard disk drives and compact disk read only memories(CD-ROMs) or digital versatile disks (DVDs).

Although an exemplary embodiment of the present disclosure has beendescribed in detail, those skilled in the art will understand thatvarious changes, substitutions, variations, and improvements disclosedherein may be made without departing from the spirit and scope of thedisclosure in its broadest form.

None of the description in the present application should be read asimplying that any particular element, step, or function is an essentialelement which must be included in the claim scope: the scope of patentedsubject matter is defined only by the allowed claims. Moreover, none ofthese claims are intended to invoke paragraph six of 35 USC § 112 unlessthe exact words “means for” are followed by a participle.

What is claimed is:
 1. A method for fault detection of a buildingautomation system using deep learning comprising the steps of:maintaining a system manager application in a data processing system ofa management system configured to perform building automation systemfunctions and to provide a graphical user interface; and running thesystem manager application to: receive point data for a hardware beinganalyzed, wherein the received point data is contaminated data; train adeep learning model for the hardware being analyzed; generate predicteddata based on the deep learning model; analyze the predicted data andthe received point data; identify a fault in the hardware being analyzedaccording to the received point data and the predicted data; and producea fault report according to the identified fault.
 2. The method of claim1, wherein the received data includes at least required points for thehardware being analyzed.
 3. The method of claim 1, wherein training thedeep learning model includes applying a Huber loss function.
 4. Themethod of claim 1, wherein training the deep learning model includesapplying dropout techniques for regularization.
 5. The method of claim1, wherein analyzing the predicted data and the received point dataincludes include applying cumulative sum control chart (CUSUM)sequential analysis for summing, weighting, and change detection.
 6. Themethod of claim 1, wherein the system manager application alsonormalizes some or all of the predicted data or the received point data.7. The method of claim 1, wherein identifying the fault includescomparing the received point data for a first period of time with thepredicted data for a corresponding second period of time.
 8. The methodof claim 1, wherein identifying the fault includes identifying when anormalized deviation between received point data and the predicted datais greater than more than a predetermined threshold.
 9. The method ofclaim 1, wherein the fault report is a graphic user interfaceillustrating the received point data as compared to the predicted data.10. A non-transitory computer-readable medium encoded with executableinstructions that is configured to run in a data processing system of amanagement system, configured to perform building automation systemfunctions, and configured to provide a graphical user interface, whereinthe building automation system functions include: receiving point datafor a hardware being analyzed, wherein the received point data iscontaminated data; training a deep learning model for the hardware beinganalyzed; generating predicted data based on the deep learning model;analyzing the predicted data and the received point data; identifying afault in the received point data with respect to the predicted data; andproducing a fault report according to the identified fault.
 11. Thenon-transitory computer-readable medium of claim 10, wherein thereceived data includes at least required points for the hardware beinganalyzed.
 12. The non-transitory computer-readable medium of claim 10,wherein training the deep learning model includes applying a Huber lossfunction.
 13. The non-transitory computer-readable medium of claim 10,wherein training the deep learning model includes applying dropouttechniques for regularization.
 14. The non-transitory computer-readablemedium of claim 10, wherein analyzing the predicted data and thereceived point data includes include applying cumulative sum controlchart (CUSUM) sequential analysis for summing, weighting, and changedetection.
 15. The non-transitory computer-readable medium of claim 10,wherein the building manager system functions also include normalizingsome or all of the predicted data or the received point data.
 16. Thenon-transitory computer-readable medium of claim 10, wherein identifyingthe fault includes comparing the received point data for a first periodof time with the predicted data for a corresponding second period oftime.
 17. The non-transitory computer-readable medium of claim 10,wherein identifying the fault includes identifying when a normalizeddeviation between received point data and the predicted data is greaterthan more than a predetermined threshold.
 18. The non-transitorycomputer-readable medium of claim 10, wherein the fault report is agraphic user interface illustrating the received point data as comparedto the predicted data.
 19. A building automation system comprising adata processing system, and a plurality of devices, sensors, andactuators, wherein the data processing system includes a graphical userinterface and executes a system manager application to perform buildingautomation system functions and to: receive point data for a hardwarebeing analyzed, wherein the received point data is contaminated data;train a deep learning model for the hardware being analyzed; generatepredicted data based on the deep learning model; analyze the predicteddata and the received point data; identify a fault in the hardware beinganalyzed according to the received point data and the predicted data;and produce a fault report according to the identified fault.
 20. Thebuilding automation system of claim 19, wherein training the deeplearning model includes applying a Huber loss function and dropouttechniques for regularization.